LOCALIZACIÓN DE LOS CENTROS DE PRODUCCIÓN

The United States Marine Corps is growing concerned about expenditures generated from the organizational and intermediate (O&I) levels; moreover, Romero and Elliott believe efforts to reduce budgetary impact on O&S must be taken before it is to late (Romero and Elliott, 2009). Romero and Elliott began their thesis, Developing a United States Marine Corps Organizational and Intermediate Level Maintenance Performance Cost Model, by noting a multitude of O&S cost drivers, such as inventory, OPTEMPO, and equipment age, procurement costs that are not within the scope of decision-makers. Furthermore, Romero and Elliott (2009) suggested that by developing a method to understand and analyze the relationship between cost variations and the continued increases in spending, DoD could support sustainment budgetary requirements in the annual funding process. In this manner, budgetary planners could have a more reliable way to forecast future budgets, especially during times of monetary uncertainty. Romero and Elliott (2009) presented an example about how overestimating inventory has created extra spending with the Marine Corps. With the end of operations in Iraq and a drawdown over the horizon at Afghanistan, a question must be asked: What is going to happen to the inventory built to sustain the wars? The DoD has created inventories to sustain operations, so the question is this: When is the right time to take the foot off the gas, particularly when war itself is so unpredictable and may not present an exact final day? Questions like these are extremely important to our project because the costs associated with the sustainment of operations can be vastly complex and variable Romero and Elliott (2009) covered the importance of identifying the very aspects that can be affected by the lowest level of maintenance.

According to Dixon (2006) in The Maintenance Costs of Aging Aircraft: Insights From Commercial Aviation, a close study of how commercial aircrafts age could help military decision-makers understand how “aging effects” affect cost estimation over time. In the cost study, Dixon (2006) covered three separate linear regressions by computing age effects on aircraft age zero to six years old; the second, aircraft age six to 12; and lastly, aircraft age 12 and older. Dixon (2006) displayed the results of the RAND study as follows: group one shows a maintenance increase cost rate of 17.6% per year; group two

displays an annually increase rate of 3.5% per year; and group three yields a surprising 0.7% increase per year. Dixon (2006) also explained that organizations must assume a rapid constant increase in cost with age; however, other studies show that such assumptions are incorrect. Furthermore, the reason the younger aircraft result is higher than the rest is due to a cost-shift from manufacturer-provided maintenance to owner- provided maintenance after the warranties have expired (Dixon, 2006). Dixon’s point is that leadership in the military must spot such changes while projecting future budgets not as a linearly increasing cost but as a midway point at which costs needs to be reevaluated. Utilizing flight hours to calculate the life of an airframe and its components is the most widely used and accepted method of measurement. A linear relationship is assumed to exist, along with the assumption that all parts on the aircraft have constant failure rates. These assumptions do not factor into the age of the weapon system or components, or into the change in mission or utilization of the weapon system and its components. In our analysis of the GCU’s data, we hoped to identify trends in the failure rates and make correlations to the age and/or utilization of systems the GCUs support. In A Method of Forecasting Repair and Replacement Needs for Naval Aircraft: Phase II, DeLozier and Wilkinson (1986) defined the variables that could be used in a method for forecasting repair and replacement needs for naval aircraft phase II. These variables include the replacement rate, fraction recycled, failure rate and repair rate.

Delozier and Wilkinson (1986) provided valuable insight to aid our interpretation of the current maintenance data. Models such as this need accurate data to predict replacement rates. Our analysis will examine the data used to determine failure rates, and fraction replaced that impact replacement rates and costs.

Understanding how to identify which costs are fixed and which costs are variable is important. This process is complicated further by the mix of funds that the DoD uses to cover expenses. Cooper and Kaplan (1988) discus costing systems which can cloud the waters and make it difficult to see what the true expenses are or how making changes to a process or systems, which will affect the costs associated with the mix of funds that are intended to, support the program or system. Understanding the impact of changes and the importance of identifying costs, as well as understanding errors in the way data is

recorded and interpreted, makes it difficult to form a plan of attack. Data collection systems which are easy to use and understand, not only by management but also by the frontline user, greatly enhance the accuracy and volume of data collected. The DoD has many systems collecting data to form an array of measures. We use multiple sources of data to examine how costs which may seem “fixed” at a high level actually vary across categories at the Organizational level.